New Internet Hot Spots? Neighborhood Effects and Internet Censorship

During the 2011 London riots, the local government called for a ban on BlackBerry Messenger Service, a key form of communication during these events. Following the riots, Prime Minister David Cameron considered a ban on social media outlets under certain circumstances. Last year, Irmak Kara tweeted as events unraveled during the Gezi Park Protests in Turkey - now, she is on trial and faces up to three years in prison for those tweets. Last month, Iran sentenced eight citizens to a combined total of 127 years in jail for posting on Facebook. At the same time, Iran’s leaders continue to use social media outlets such as Facebook, Twitter, and Instagram. This apparent contradiction highlights the often Janus-faced nature of cyber statecraft. World leaders employ cyber statecraft domestically to exert control over their citizens as well as to propagate their messages and communicate. But which states are more likely to censor and restrict access to the Internet? On the surface, this seems like a fairly straightforward question - clearly, democracies must censor less than authoritarian regimes. However, as these brief examples illustrate, global politics is rarely so straightforward. Spatial patterns may in fact impact the likelihood of Internet censorship more consistently than a state’s domestic attributes. While factors such as regime type, level of economic development, and Internet infrastructure undoubtedly play a role, a look at spatial patterns data highlights that a neighborhood “hot spot” effect may be a predominant force in a state’s propensity toward Internet censorship.

Hot spots traditionally refer to the geographic clustering of a given event, such as conflictdemocracy, or terrorism. Analysts who study hot spots argue that geography – and its diffusion effect – has a stronger impact on the occurrence of these events than domestic factors. Internet censorship may be a likely addition to the ‘hot spots’ literature. An initial investigation of geospatial data shows visible geographic clustering of Internet censorship and freedoms. However, the same linear relationship is not necessarily true between several predominant domestic indicators and Internet censorship. To evaluate these relationships, I reviewed the following indicators for 2013:

  • Regime type: Polity V’s twenty-one point ordinal measure ranking states from authoritarian to anocratic to democratic regimes.
  • Economic development: World Bank GDP per capita (PPP).
  • Internet penetration: Percentage of Individuals using the Internet from the International Telecommunications Union.
  • Freedom on the Net: Freedom House’s ranking of countries as Free, Partly Free or Not Free with regard to Internet freedoms, as well as theWeb Index’s freedom and openness indicator.

The obvious hypotheses assume that democratic regimes, greater economic development, and greater Internet penetration would be inversely related to Internet censorship. However, that’s not always the case. Let’s take democracy. While all but one country ranked as ‘Free’ (minimal or no Internet censorship) is also a democracy (Armenia is the outlier), not all democracies are ranked as ‘Free’. For example, Turkey, Brazil, South Korea, Mexico, Indonesia, and India are all ranked as ‘Partly Free’ for Internet freedoms, even though Polity categorizes them as democracies. In the realm of Internet freedoms, they join authoritarian countries like Azerbaijan and Kazakhstan as only ‘Partly Free’. The nebulous world of the anocracies is even more haphazard with various illiberal democracies exhibiting a range of censorship characteristics. In short, the countries with the greatest Internet freedoms are more likely to be democracies, but democracy does not guarantee the presence of Internet freedoms.

Similarly, economic development does not appear to be correlated with Internet censorship. Countries that display the greatest Internet censorship (i.e. ‘Not Free’) range from Ethiopia (with a GDP per capita of roughly $1300) to Saudi Arabia (with one of the highest GDP per capitas in the world). On the other end of the spectrum, countries with greater Internet freedom (i.e. ‘Free’) range from Kenya and Georgia (~$2200 and $7100 GDP per capita, respectively) along with the economic leaders United States, Australia, and Germany. The data shows that there are plenty of instances of Internet censorship on both ends of the economic development scale, and the same is true for Internet freedoms.

Finally, it seems intuitive that Internet penetration would be inversely related to Internet censorship. States that censor the Internet seem likely to also impede the development of Internet infrastructure and hinder access. Again, this may not be the case. In ‘Free’ countries Philippines, Ukraine, and South Africa, only 35-50% of the population has access to the Internet. This is the same percentage of Internet penetration found in ‘Not Free’ countries China, Uzbekistan, and Vietnam. Even at the higher levels of Internet access (~85-95%), one finds countries like the United Arab Emirates and Bahrain (Not Free) as well as Iceland and Japan (Free).

In short, many of the usual suspects such as regime type, level of economic development, and Internet penetration may not have as linear an impact on Internet censorship as is commonly assumed. Conversely, the spatial patterns (shown in these interactive maps from Freedom House and Web Index) seem quite apparent with regard to Internet censorship. For example, Africa exhibits discrete clusters of both openness and censorship, as does Asia, while Western Europe and the Middle East exhibit larger, regional clustering patterns at extreme ends of the censorship spectrum. There appears to be a neighborhood effect that may in fact more consistently influence a state’s likelihood of Internet censorship than these domestic factors.

This initial look at the data on Internet censorship highlights the need to more rigorously test many common assumptions about Internet censorship. Comprehensive quantitative analysis using spatial statistics modeling techniques could be applied to further test these hypotheses and evaluate the cross-sectional and temporal trends. These models should include additional control variables such as education levels and urbanization, temporal lags, as well as explore the potential for interactive effects between geography (i.e. contiguity) and some of the domestic factors discussed here. Until then, there’s a chance that global Internet ‘hot spots’ may soon become just as synonymous with Internet censorship as it is with Internet access.